方法对比

	鲁棒梯度提升	Boosting
领域	机器学习	机器学习
方法族	Machine learning	Machine learning
起源年份	2001	1990–1997
提出者	Friedman, J. H. (with Huber loss from Huber, P. J.)	Schapire, R. E.; Freund, Y.
类型	Ensemble (boosted trees with robust loss)	Sequential ensemble (iterative reweighting)
开创性文献	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.
别名	gradient boosting with Huber loss, robust GBM, outlier-robust boosting, robust gradient-boosted trees	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
相关	6	6
摘要	Robust Gradient Boosting is gradient boosting trained with outlier-resistant loss functions — most commonly the Huber loss or quantile (pinball) loss — instead of squared-error loss. Proposed in Friedman's seminal 2001 paper, this variant produces predictions far less distorted by extreme values or contaminated labels, while retaining the full predictive power of gradient-boosted trees.	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.